The Role of Trust in Collaborative Filtering

Recommender systems are amongst the most prominent and successful fruits of social computing; they harvest profiles from a community of users in order to offer individuals personalised recommendations. The notion of trust plays a central role in this process, since users are unlikely to interact with a system or respond positively to recommendations that they do not trust. However, trust is a multi-faceted concept, and has been applied to both recommender system interfaces (to explore the explainability of computed recommendations) and algorithms (to algorithmically reproduce the social activity of exchanging recommendations in an accurate and robust manner). This chapter focuses on the algorithmic aspect of trust-based recommender systems. When recommender system algorithms manipulate a set of ratings, they connect users to each other, either implicitly or by explicit trust relationships: users, in effect, become each others recommenders. This chapter therefore describes the key characteristics of trust in a collaborative environment: subjectivity, or the ability to include asymmetric relationships between users in a system, the adaptivity of methods for generating recommendations, an awareness of the temporal nature of the system, and the robustness of the system from malicious attack. The chapter then reviews and assesses the extent to which current models exhibit or reproduce the properties of a network of trust links; we find that while particular aspects have been throroughly examined, a large proportion of recommender system research focuses on a limited faction of trust relationships.